
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_						)						->						Union[str, Any]:
					'''simple docstring'''


					for param, grad_param in zip(model_a.parameters()      ,      model_b.parameters()						):
										if not param.requires_grad:
															continue
										if not did_step:
															# Grads should not be in sync
															assert (
															    torch.allclose(param.grad      ,      grad_param.grad						) is False
															), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
										else:
															# Grads should be in sync
															assert (
															    torch.allclose(param.grad      ,      grad_param.grad						) is True
															), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_=True						)						->						List[str]:
					'''simple docstring'''


					model.train()
					SCREAMING_SNAKE_CASE__						=					model(a__						)
					SCREAMING_SNAKE_CASE__						=					F.mse_loss(a__      ,      target.to(output.device						)						)
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(a__						)






def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_=False						)						->						int:
					'''simple docstring'''


					set_seed(42						)
					SCREAMING_SNAKE_CASE__						=					RegressionModel()
					SCREAMING_SNAKE_CASE__						=					deepcopy(a__						)
					SCREAMING_SNAKE_CASE__						=					RegressionDataset(length=80						)
					SCREAMING_SNAKE_CASE__						=					DataLoader(a__      ,      batch_size=16						)
					model.to(accelerator.device						)
					if sched:
										SCREAMING_SNAKE_CASE__						=					AdamW(params=model.parameters()      ,      lr=1e-3						)
										SCREAMING_SNAKE_CASE__						=					AdamW(params=ddp_model.parameters()      ,      lr=1e-3						)
										SCREAMING_SNAKE_CASE__						=					LambdaLR(a__      ,      lr_lambda=lambda UpperCamelCase_						: epoch**0.65						)
										SCREAMING_SNAKE_CASE__						=					LambdaLR(a__      ,      lr_lambda=lambda UpperCamelCase_						: epoch**0.65						)
					# Make a copy of `model`
					if sched:
										SCREAMING_SNAKE_CASE__						=					accelerator.prepare(a__      ,      a__      ,      a__      ,      a__						)
					else:
										SCREAMING_SNAKE_CASE__						=					accelerator.prepare(a__      ,      a__						)
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader






def 				_lowercase				(	UpperCamelCase_						)						->						Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					get_training_setup(a__						)
					# Use a single batch
					SCREAMING_SNAKE_CASE__						=					next(iter(a__						)						).values()
					for iteration in range(3						):
										# Gather the distributed inputs and targs for the base model
										SCREAMING_SNAKE_CASE__						=					accelerator.gather((ddp_input, ddp_target)						)
										SCREAMING_SNAKE_CASE__						=					input.to(accelerator.device						), target.to(accelerator.device						)
										# Perform our initial ground truth step in non "DDP"
										step_model(a__      ,      a__      ,      a__      ,      a__						)
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(a__						):
																				step_model(a__      ,      a__      ,      a__      ,      a__						)
										else:
															# Sync grads
															step_model(a__      ,      a__      ,      a__      ,      a__						)

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(a__      ,      a__      ,      a__      ,      a__						)
										for param, ddp_param in zip(model.parameters()      ,      ddp_model.parameters()						):
															if not param.requires_grad:
																				continue
															assert torch.allclose(
															    param.grad      ,      ddp_param.grad						), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration						)
										SCREAMING_SNAKE_CASE__						=					ddp_input[torch.randperm(len(a__						)						)]






def 				_lowercase				(	UpperCamelCase_						)						->						Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					get_training_setup(a__						)
					# Use a single batch
					SCREAMING_SNAKE_CASE__						=					next(iter(a__						)						).values()
					for iteration in range(3						):
										# Gather the distributed inputs and targs for the base model
										SCREAMING_SNAKE_CASE__						=					accelerator.gather((ddp_input, ddp_target)						)
										SCREAMING_SNAKE_CASE__						=					input.to(accelerator.device						), target.to(accelerator.device						)
										# Perform our initial ground truth step in non "DDP"
										step_model(a__      ,      a__      ,      a__      ,      a__						)
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(a__						):
																				step_model(a__      ,      a__      ,      a__      ,      a__						)
										else:
															# Sync grads
															step_model(a__      ,      a__      ,      a__      ,      a__						)

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters()      ,      ddp_model.parameters()						):
															if not param.requires_grad:
																				continue
															if iteration % 2 == 0:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad      ,      ddp_param.grad						) is False
																				), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
															else:
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad      ,      ddp_param.grad						) is True
																				), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration						)
										SCREAMING_SNAKE_CASE__						=					ddp_input[torch.randperm(len(a__						)						)]






def 				_lowercase				(	UpperCamelCase_=False      ,      UpperCamelCase_=False						)						->						Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					Accelerator(
					    split_batches=a__      ,      dispatch_batches=a__      ,      gradient_accumulation_steps=2						)
					# Test that context manager behaves properly
					SCREAMING_SNAKE_CASE__						=					get_training_setup(a__						)
					for iteration, batch in enumerate(a__						):
										SCREAMING_SNAKE_CASE__						=					batch.values()
										# Gather the distributed inputs and targs for the base model
										SCREAMING_SNAKE_CASE__						=					accelerator.gather((ddp_input, ddp_target)						)
										SCREAMING_SNAKE_CASE__						=					input.to(accelerator.device						), target.to(accelerator.device						)
										# Perform our initial ground truth step in non "DDP"
										step_model(a__      ,      a__      ,      a__      ,      a__      ,      a__						)
										# Do "gradient accumulation" (noop)
										with accelerator.accumulate(a__						):
															step_model(a__      ,      a__      ,      a__      ,      a__						)

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters()      ,      ddp_model.parameters()						):
															if not param.requires_grad:
																				continue
															if ((iteration + 1) % 2 == 0) or (iteration == len(a__						) - 1):
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad      ,      ddp_param.grad						) is True
																				), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
															else:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad      ,      ddp_param.grad						) is False
																				), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration						)
										SCREAMING_SNAKE_CASE__						=					ddp_input[torch.randperm(len(a__						)						)]
					GradientState._reset_state()






def 				_lowercase				(	UpperCamelCase_=False      ,      UpperCamelCase_=False						)						->						int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					Accelerator(
					    split_batches=a__      ,      dispatch_batches=a__      ,      gradient_accumulation_steps=2						)
					# Test that context manager behaves properly
					SCREAMING_SNAKE_CASE__						=					get_training_setup(a__      ,      a__						)
					for iteration, batch in enumerate(a__						):
										SCREAMING_SNAKE_CASE__						=					batch.values()
										# Gather the distributed inputs and targs for the base model
										SCREAMING_SNAKE_CASE__						=					accelerator.gather((ddp_input, ddp_target)						)
										SCREAMING_SNAKE_CASE__						=					input.to(accelerator.device						), target.to(accelerator.device						)
										# Perform our initial ground truth step in non "DDP"
										model.train()
										ddp_model.train()
										step_model(a__      ,      a__      ,      a__      ,      a__      ,      a__						)
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__						)):
															if split_batches:
																				sched.step()
															else:
																				for _ in range(accelerator.num_processes						):
																									sched.step()
										opt.zero_grad()
										# Perform gradient accumulation under wrapper
										with accelerator.accumulate(a__						):
															step_model(a__      ,      a__      ,      a__      ,      a__						)
															ddp_opt.step()
															ddp_sched.step()
															ddp_opt.zero_grad()

										# Learning rates should be the same
										assert (
										    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
										), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
										SCREAMING_SNAKE_CASE__						=					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__						))
										if accelerator.num_processes > 1:
															check_model_parameters(a__      ,      a__      ,      a__      ,      a__						)
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration						)
					GradientState._reset_state()






def 				_lowercase				(	)						->						Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					Accelerator()

					SCREAMING_SNAKE_CASE__						=					RegressionDataset(length=80						)
					SCREAMING_SNAKE_CASE__						=					DataLoader(a__      ,      batch_size=16						)
					SCREAMING_SNAKE_CASE__						=					RegressionDataset(length=96						)
					SCREAMING_SNAKE_CASE__						=					DataLoader(a__      ,      batch_size=16						)
					SCREAMING_SNAKE_CASE__						=					accelerator.prepare(a__      ,      a__						)
					assert accelerator.gradient_state.active_dataloader is None
					for iteration, _ in enumerate(a__						):
										assert id(accelerator.gradient_state.active_dataloader						) == id(a__						)
										if iteration < len(a__						) - 1:
															assert not accelerator.gradient_state.end_of_dataloader
															if iteration == 1:
																				for batch_num, _ in enumerate(a__						):
																									assert id(accelerator.gradient_state.active_dataloader						) == id(a__						)
																									if batch_num < len(a__						) - 1:
																														assert not accelerator.gradient_state.end_of_dataloader
																									else:
																														assert accelerator.gradient_state.end_of_dataloader
										else:
															assert accelerator.gradient_state.end_of_dataloader
					assert accelerator.gradient_state.active_dataloader is None






def 				_lowercase				(	)						->						Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					Accelerator()
					SCREAMING_SNAKE_CASE__						=					accelerator.state
					if state.local_process_index == 0:
										print('**Test `accumulate` gradient accumulation with dataloader break**'						)
					test_dataloader_break()
					if state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print('**Test NOOP `no_sync` context manager**'						)
										test_noop_sync(a__						)
					if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
										if state.local_process_index == 0:
															print('**Test Distributed `no_sync` context manager**'						)
										test_distributed_sync(a__						)
					if state.distributed_type == DistributedType.MULTI_GPU:
										for split_batch in [True, False]:
															for dispatch_batches in [True, False]:
																				if state.local_process_index == 0:
																									print(
																									    '**Test `accumulate` gradient accumulation, '      ,      F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'      ,      )
																				test_gradient_accumulation(a__      ,      a__						)

    # Currently will break on torch 2.0 +, need to investigate why
					if is_torch_version('<'      ,      '2.0'						) or state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print(
															    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '      ,      '`split_batches=False`, `dispatch_batches=False`**'      ,      )
										test_gradient_accumulation_with_opt_and_scheduler()
										if state.distributed_type == DistributedType.MULTI_GPU:
															for split_batch in [True, False]:
																				for dispatch_batches in [True, False]:
																									if not split_batch and not dispatch_batches:
																														continue
																									if state.local_process_index == 0:
																														print(
																														    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '      ,      F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'      ,      )
																									test_gradient_accumulation_with_opt_and_scheduler(a__      ,      a__						)






def 				_lowercase				(	UpperCamelCase_						)						->						Optional[Any]:
					'''simple docstring'''


					main()


if __name__ == "__main__":
				main()













"""simple docstring"""


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from transformers.modeling_outputs import BaseModelOutput

		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	Union[str, Any]     , **UpperCAmelCase							:	List[Any]   )	->       int:
											super().__init__(**UpperCAmelCase   )

											if self.framework == "tf":
																	raise ValueError(f"""The {self.__class__} is only available in PyTorch."""   )

											requires_backends(self     , 'vision'   )
											self.check_model_type(UpperCAmelCase   )
					def __call__(							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, "Image.Image", List[Dict[str, Any]]]     , UpperCAmelCase							:	Union[str, List[str]] = None     , **UpperCAmelCase							:	Dict     , )	->       Any:
											if "text_queries" in kwargs:
																	lowerCAmelCase   :Dict						=			kwargs.pop('text_queries'   )

											if isinstance(UpperCAmelCase     , (str, Image.Image)   ):
																	lowerCAmelCase   :List[str]						=			{'image': image, 'candidate_labels': candidate_labels}
											else:
																	lowerCAmelCase   :Union[str, Any]						=			image
											lowerCAmelCase   :Optional[int]						=			super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
											return results
					def        UpperCAmelCase__ (							self							:	List[Any]     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											lowerCAmelCase   :Any						=			{}
											if "threshold" in kwargs:
																	lowerCAmelCase   :int						=			kwargs['threshold']
											if "top_k" in kwargs:
																	lowerCAmelCase   :str						=			kwargs['top_k']
											return {}, {}, postprocess_params
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Union[str, Any]   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(inputs['image']   )
											lowerCAmelCase   :Dict						=			inputs['candidate_labels']
											if isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																	lowerCAmelCase   :str						=			candidate_labels.split(','   )

											lowerCAmelCase   :str						=			torch.tensor([[image.height, image.width]]     , dtype=torch.intaa   )
											for i, candidate_label in enumerate(UpperCAmelCase   ):
																	lowerCAmelCase   :Any						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																	lowerCAmelCase   :Optional[Any]						=			self.image_processor(UpperCAmelCase     , return_tensors=self.framework   )
																	yield {
																	    "is_last": i == len(UpperCAmelCase   ) - 1,
																	    "target_size": target_size,
																	    "candidate_label": candidate_label,
																	    **text_inputs,
																	    **image_features,
																	}
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       List[Any]:
											lowerCAmelCase   :Union[str, Any]						=			model_inputs.pop('target_size'   )
											lowerCAmelCase   :List[str]						=			model_inputs.pop('candidate_label'   )
											lowerCAmelCase   :Union[str, Any]						=			model_inputs.pop('is_last'   )

											lowerCAmelCase   :int						=			self.model(**UpperCAmelCase   )

											lowerCAmelCase   :Any						=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	int=0.1     , UpperCAmelCase							:	Optional[Any]=None   )	->       str:
											lowerCAmelCase   :int						=			[]
											for model_output in model_outputs:
																	lowerCAmelCase   :int						=			model_output['candidate_label']
																	lowerCAmelCase   :List[Any]						=			BaseModelOutput(UpperCAmelCase   )
																	lowerCAmelCase   :Dict						=			self.image_processor.post_process_object_detection(
																	    outputs=UpperCAmelCase     , threshold=UpperCAmelCase     , target_sizes=model_output['target_size']   )[0]

																	for index in outputs["scores"].nonzero():
																							lowerCAmelCase   :Tuple						=			outputs['scores'][index].item()
																							lowerCAmelCase   :int						=			self._get_bounding_box(outputs['boxes'][index][0]   )

																							lowerCAmelCase   :Optional[int]						=			{'score': score, 'label': label, 'box': box}
																							results.append(UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			sorted(UpperCAmelCase     , key=lambda UpperCAmelCase   : x["score"]     , reverse=UpperCAmelCase   )
											if top_k:
																	lowerCAmelCase   :Dict						=			results[:top_k]

											return results
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	"torch.Tensor"   )	->       Dict[str, int]:
											if self.framework != "pt":
																	raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			box.int().tolist()
											lowerCAmelCase   :Union[str, Any]						=			{
											    'xmin': xmin,
											    'ymin': ymin,
											    'xmax': xmax,
											    'ymax': ymax,
											}
											return bbox